CN114065811A - Composite insulator detection method and device, terminal equipment and readable storage medium - Google Patents

Composite insulator detection method and device, terminal equipment and readable storage medium Download PDF

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Publication number
CN114065811A
CN114065811A CN202111328998.4A CN202111328998A CN114065811A CN 114065811 A CN114065811 A CN 114065811A CN 202111328998 A CN202111328998 A CN 202111328998A CN 114065811 A CN114065811 A CN 114065811A
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composite insulator
recognition model
data
training
taking
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CN202111328998.4A
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Chinese (zh)
Inventor
汪林立
梁永纯
黄丰
董重里
岳楹超
吕旺燕
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Guangdong Power Grid Co Ltd
Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Guangdong Power Grid Co Ltd
Electric Power Research Institute of Guangdong Power Grid Co Ltd
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Priority to CN202111328998.4A priority Critical patent/CN114065811A/en
Publication of CN114065811A publication Critical patent/CN114065811A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
    • G01R31/1245—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of line insulators or spacers, e.g. ceramic overhead line cap insulators; of insulators in HV bushings
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G06F18/24155—Bayesian classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Abstract

The invention discloses a composite insulator detection method, a device, a terminal device and a readable storage medium, wherein the method comprises the following steps: acquiring an ultrasonic signal of the sample composite insulator as sample data; extracting the characteristics of the sample data to generate corresponding signal characteristic data; establishing a recognition model, and training the recognition model by using the signal characteristic data; correcting the network parameters of the trained recognition model until the recognition accuracy of the recognition model meets the preset condition, and taking the corresponding recognition model as a target model; and detecting the composite insulator to be detected by using the target model. The composite insulator detection method provided by the invention can be used for rapidly judging whether the composite insulator has defects or not and providing the type, size and position information of the defects, and has the advantages of high identification speed and high accuracy.

Description

Composite insulator detection method and device, terminal equipment and readable storage medium
Technical Field
The invention relates to the technical field of composite insulator detection, in particular to a composite insulator detection method, a composite insulator detection device, terminal equipment and a readable storage medium.
Background
The composite insulator has been widely used in power transmission lines due to its advantages of small volume, high density, high mechanical properties, excellent contamination resistance, good insulation, etc. The composite insulator mainly comprises a core rod, a sheath, an umbrella skirt and end metal accessories, wherein the umbrella skirt sheath mainly comprises silicon rubber or other component composite materials, the surface of the umbrella skirt is externally insulated and can provide required creepage distance, and the core rod is generally epoxy resin glass fiber (glass fiber reinforced plastic rod) and has higher mechanical strength. In the manufacturing process of the composite insulator, various hidden defects are left on the composite insulator due to the quality problem of materials and the improper manufacturing process, including bubbles in rubber, cracking of the rubber and the core rod, debonding of the core rod and the umbrella skirt and the like. Because the defects are quite hidden and cannot be directly found from the appearance, partial discharge can be caused under the electromagnetic environment with high field intensity, so that the performance of the composite insulator is deteriorated, the problems of insulation breakdown, flashover, core rod fracture and the like are caused, and huge potential safety hazards are left for the operation of a power transmission line.
Currently, an ultrasonic detection method is one of the most commonly used detection methods for composite insulators, and is mainly used for judging whether defects exist in the composite insulators or not according to detected ultrasonic signals. However, the existing ultrasonic inspection of composite insulators often has high technical requirements on inspection technicians, and technicians are required to analyze ultrasonic signals according to experience and then judge whether the sample has defects, types, sizes, positions and the like of the defects, and the method relying on manual analysis not only has large difference and low efficiency between detection results due to uneven capabilities of the inspection technicians, but also has large deviation between the analysis results and the real situation, even causes the problems of missed inspection, wrong inspection and the like of the defects, and is further not beneficial to maintenance of the power transmission line.
Disclosure of Invention
The invention aims to provide a composite insulator detection method, a composite insulator detection device, terminal equipment and a readable storage medium, and aims to solve the problems of low efficiency and large error of a composite insulator detection method depending on manual analysis in the prior art.
In order to achieve the above object, the present invention provides a method for detecting a composite insulator, comprising:
acquiring an ultrasonic signal of the sample composite insulator as sample data;
extracting the characteristics of the sample data to generate corresponding signal characteristic data;
establishing a recognition model, and training the recognition model by using the signal characteristic data;
correcting the network parameters of the trained recognition model until the recognition accuracy of the recognition model meets the preset condition, and taking the corresponding recognition model as a target model;
and detecting the composite insulator to be detected by using the target model.
Further, preferably, the performing feature extraction on the sample data to generate corresponding signal feature data includes:
and performing feature extraction on the sample data by using empirical mode decomposition, and taking the generated content modal component as signal feature data.
Further, preferably, the establishing the identification model includes:
and establishing a recognition model by using a BP neural network, a naive Bayes method, a decision tree or a support vector machine.
Further, preferably, the training the recognition model by using the signal feature data includes:
and training the recognition model by taking the signal characteristic data as input and taking the composite insulator defect type data as output.
The present invention also provides a composite insulator detection apparatus, comprising:
the sample acquisition unit is used for acquiring an ultrasonic signal of the sample composite insulator as sample data;
the characteristic extraction unit is used for extracting the characteristics of the sample data and generating corresponding signal characteristic data;
the training unit is used for establishing a recognition model and training the recognition model by utilizing the signal characteristic data;
the correction unit is used for correcting the network parameters of the trained recognition model until the recognition accuracy of the recognition model meets a preset condition, and taking the corresponding recognition model as a target model;
and the detection unit is used for detecting the composite insulator to be detected by utilizing the target model.
Further, preferably, the feature extraction unit is further configured to:
and performing feature extraction on the sample data by using empirical mode decomposition, and taking the generated content modal component as signal feature data.
Further, preferably, the training unit is further configured to:
and establishing a recognition model by using a BP neural network, a naive Bayes method, a decision tree or a support vector machine.
Further, preferably, the training unit is further configured to:
and training the recognition model by taking the signal characteristic data as input and taking the composite insulator defect type data as output.
The present invention also provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the composite insulator detection method as described in any one of the above.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a composite insulator detection method as described in any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a composite insulator detection method, a device, a terminal device and a readable storage medium, wherein the method comprises the following steps: acquiring an ultrasonic signal of the sample composite insulator as sample data; extracting the characteristics of the sample data to generate corresponding signal characteristic data; establishing a recognition model, and training the recognition model by using the signal characteristic data; correcting the network parameters of the trained recognition model until the recognition accuracy of the recognition model meets the preset condition, and taking the corresponding recognition model as a target model; and detecting the composite insulator to be detected by using the target model.
The composite insulator detection method provided by the invention can be used for rapidly judging whether the composite insulator has defects or not, providing the type, size and position information of the defects, avoiding the conditions of false detection and false judgment of the defects of the composite insulator caused by technical deficiency of detection technicians, realizing intelligent identification during ultrasonic detection of the internal defects of the composite insulator, and having the advantages of high identification speed and high accuracy.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a composite insulator detection method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a composite insulator detection apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, an embodiment of the invention provides a method for detecting a composite insulator. As shown in fig. 1, the composite insulator detection method includes steps S10 to S50. The concrete contents of the steps are as follows:
and S10, acquiring the ultrasonic signal of the sample composite insulator as sample data.
In the step, a large number of composite insulators in various states, including composite insulators which are intact and have various defects, are mainly collected, ultrasonic detection (non-charged) is performed on the insulators, and a data sample library is established by using obtained echo signal (A-type display) original data. For the composite insulator containing defects, the defect types mainly comprise rubber internal air holes, rubber internal sections, core rod debonding, core rod fracture and the like.
And S20, performing feature extraction on the sample data to generate corresponding signal feature data.
In this step, based on the sample data obtained in step S10, feature extraction is performed on the sample data mainly by using an empirical mode decomposition method, and the generated content modal component is used as signal feature data.
It should be noted that an Empirical Mode Decomposition (EMD) method is a novel adaptive signal time-frequency processing method, and is particularly suitable for analysis processing of nonlinear non-stationary signals. The key of the method is empirical Mode decomposition, which can decompose a complex signal into a finite number of eigenmode functions (IMFs for short), and each decomposed IMF component contains local characteristic signals of different time scales of an original signal. The empirical mode decomposition carries out signal decomposition according to the time scale characteristics of data, and any basis function is not required to be preset, so that the empirical mode decomposition is fundamentally different from Fourier decomposition and wavelet decomposition methods established on prior harmonic basis functions and wavelet basis functions, the EMD method can be applied to decomposition of any type of signals in theory, particularly has obvious advantages in processing non-stationary and non-linear data, is suitable for analyzing non-linear and non-stationary signal sequences, and has high signal-to-noise ratio.
In an optional embodiment, before performing step S20, to obtain higher quality sample data, the method further includes: and performing data preprocessing on the sample original data obtained in the step S10 to obtain sample data with higher quality. Optionally, the preprocessing is to process the sample data by using wavelet transform. It should be noted that the wavelet transform has a good effect in processing the non-stationary nonlinear signal, and therefore, in the present embodiment, the wavelet transform can be used to perform denoising processing on the original data. After the denoising process is performed, in step S20, feature extraction may be performed on the denoised sample data by using empirical mode decomposition, and the generated content modal component is used as signal feature data.
And S30, establishing a recognition model, and training the recognition model by using the signal characteristic data.
It should be noted that, in a specific embodiment, the identification model is established mainly by using a BP neural network, a naive bayes method, a decision tree, or a support vector machine.
Specifically, when step S30 is executed, the collected signal feature data of the defects and defect waveforms of different composite insulators may be trained by using naive bayes method, decision tree, support vector machine, neural network, and the like, to obtain corresponding recognition models. For example, when a BP neural network is used to construct the recognition model, the following contents are specifically included:
1) constructing a neural network with 3-5 layers;
2) the number of the input nodes can select a characteristic vector reflecting 1 multiplied by 10 of the ultrasonic echo signals, namely signal characteristic data as input, and the number of the corresponding input nodes is 10;
3) the number of the output nodes is 5, and the output nodes correspond to five defects of different types of the composite insulator;
4) selecting a sigmoid function as a transfer function of a hidden layer, and using log-sigmoid as a transfer function of an output layer;
5) in the actual training process, methods such as Batch Normalization and Dropout can be used, and methods such as regularization and the like can be used for reducing overfitting, so that a corresponding recognition model is obtained.
And S40, correcting the network parameters of the trained recognition model until the recognition accuracy of the recognition model meets the preset condition, and taking the corresponding recognition model as a target model.
It can be understood that, assuming that a recognition model is constructed by using a BP neural network, when the recognition model is trained for a preset number of times (for example, 120 times), the training result at this time needs to be checked, that is, the recognition accuracy of the current recognition model is checked, when the recognition model is detected, if the recognition accuracy of the recognition model can reach a preset value, for example, 95%, the recognition model can be used as a target model for detecting other composite insulators, and if the recognition accuracy of the recognition model cannot reach the preset value, sample data can be used for further training until the recognition accuracy reaches the preset value. It should be noted that 95% is only a preferable way of the recognition accuracy in this embodiment, and the value may be set according to needs in practical applications, which is not limited herein.
And S50, detecting the composite insulator to be detected by using the target model.
In this step, after the target model is obtained in step S40, the target model may be written as a program for identifying the corresponding defect. When the composite insulator to be detected is detected, firstly, an ultrasonic detector is used for detecting a composite insulator sample, and ultrasonic signals measured in each measurement are transmitted into a program in real time for defect identification; if all the measurement points are judged to be normal, the composite insulator is free of defects and can be put into use; if the corresponding measuring point data is judged to have defects through a program, subsequent defect treatment can be carried out according to the judged defect information. For example, the position of the program output defect is positioned on the core rod, so that whether the defect exists can be further confirmed by physically cutting off the composite insulator to be detected according to the identification result.
The composite insulator detection method provided by the embodiment of the invention can quickly judge whether the composite insulator has defects and provide the type, size and position information of the defects, thereby avoiding the conditions of false detection and false judgment of the defects of the composite insulator caused by technical deficiency of detection technicians, realizing intelligent identification when the defects in the composite insulator are detected by ultrasonic waves, and having the advantages of high identification speed and high accuracy.
Referring to fig. 2, an embodiment of the present invention further provides a composite insulator testing apparatus, including:
the sample acquisition unit 01 is used for acquiring an ultrasonic signal of the sample composite insulator as sample data;
a feature extraction unit 02, configured to perform feature extraction on the sample data to generate corresponding signal feature data;
the training unit 03 is used for establishing a recognition model and training the recognition model by using the signal characteristic data;
the correcting unit 04 is configured to correct the network parameters of the trained recognition model until the recognition accuracy of the recognition model meets a preset condition, and use the corresponding recognition model as a target model;
and the detection unit 05 is used for detecting the composite insulator to be detected by using the target model.
In a specific embodiment, the feature extraction unit 02 is further configured to:
and performing feature extraction on the sample data by using empirical mode decomposition, and taking the generated content modal component as signal feature data.
In a specific embodiment, the training unit 03 is further configured to:
and establishing a recognition model by using a BP neural network, a naive Bayes method, a decision tree or a support vector machine.
In a specific embodiment, the training unit 03 is further configured to:
and training the recognition model by taking the signal characteristic data as input and taking the composite insulator defect type data as output.
It can be understood that the composite insulator detection apparatus provided by the embodiment of the present invention is used for executing the composite insulator detection method according to any one of the above embodiments. Whether this embodiment can the short-term test composite insulator exist the defect to provide type, size and positional information of defect, avoided detecting technical staff because the technique is not enough to cause the false retrieval of composite insulator defect, the erroneous judgement condition, intelligent identification when having realized ultrasonic detection composite insulator internal defect has the advantage that the recognition rate is fast, and the degree of accuracy is high.
Referring to fig. 3, an embodiment of the present invention further provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the composite insulator detection method as described above.
The processor is used for controlling the overall operation of the terminal equipment so as to complete all or part of the steps of the composite insulator detection method. The memory is used to store various types of data to support operation at the terminal device, and these data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In an exemplary embodiment, the terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the composite insulator detection method according to any one of the above embodiments, and achieve the technical effects consistent with the above method.
In another exemplary embodiment, a computer readable storage medium is also provided, which comprises a computer program, which when executed by a processor, performs the steps of the composite insulator detection method according to any one of the above embodiments. For example, the computer-readable storage medium may be the above-mentioned memory including a computer program, and the above-mentioned computer program may be executed by a processor of a terminal device to implement the composite insulator detection method according to any one of the above-mentioned embodiments, and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A composite insulator detection method is characterized by comprising the following steps:
acquiring an ultrasonic signal of the sample composite insulator as sample data;
extracting the characteristics of the sample data to generate corresponding signal characteristic data;
establishing a recognition model, and training the recognition model by using the signal characteristic data;
correcting the network parameters of the trained recognition model until the recognition accuracy of the recognition model meets the preset condition, and taking the corresponding recognition model as a target model;
and detecting the composite insulator to be detected by using the target model.
2. The method according to claim 1, wherein the performing feature extraction on the sample data to generate corresponding signal feature data comprises:
and performing feature extraction on the sample data by using empirical mode decomposition, and taking the generated content modal component as signal feature data.
3. The method according to claim 1, wherein the establishing of the identification model comprises:
and establishing a recognition model by using a BP neural network, a naive Bayes method, a decision tree or a support vector machine.
4. The method according to claim 1, wherein the training of the recognition model using the signal feature data comprises:
and training the recognition model by taking the signal characteristic data as input and taking the composite insulator defect type data as output.
5. The utility model provides a composite insulator detection device which characterized in that includes:
the sample acquisition unit is used for acquiring an ultrasonic signal of the sample composite insulator as sample data;
the characteristic extraction unit is used for extracting the characteristics of the sample data and generating corresponding signal characteristic data;
the training unit is used for establishing a recognition model and training the recognition model by utilizing the signal characteristic data;
the correction unit is used for correcting the network parameters of the trained recognition model until the recognition accuracy of the recognition model meets a preset condition, and taking the corresponding recognition model as a target model;
and the detection unit is used for detecting the composite insulator to be detected by utilizing the target model.
6. The composite insulator detection apparatus of claim 5, wherein the feature extraction unit is further configured to:
and performing feature extraction on the sample data by using empirical mode decomposition, and taking the generated content modal component as signal feature data.
7. The composite insulator testing apparatus of claim 5, wherein said training unit is further configured to:
and establishing a recognition model by using a BP neural network, a naive Bayes method, a decision tree or a support vector machine.
8. The composite insulator testing apparatus of claim 5, wherein said training unit is further configured to:
and training the recognition model by taking the signal characteristic data as input and taking the composite insulator defect type data as output.
9. A terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the composite insulator detection method of any one of claims 1-4.
10. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when being executed by a processor, is adapted to carry out the method of composite insulator detection according to any one of claims 1-4.
CN202111328998.4A 2021-11-10 2021-11-10 Composite insulator detection method and device, terminal equipment and readable storage medium Pending CN114065811A (en)

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Application Number Priority Date Filing Date Title
CN202111328998.4A CN114065811A (en) 2021-11-10 2021-11-10 Composite insulator detection method and device, terminal equipment and readable storage medium

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Publication Number Publication Date
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116087345A (en) * 2023-04-06 2023-05-09 广东电网有限责任公司揭阳供电局 Method, device and medium for calculating axial defect length of composite insulator
CN116466667A (en) * 2023-04-20 2023-07-21 成都工业职业技术学院 Intelligent control method, system and storage medium for part machining

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116087345A (en) * 2023-04-06 2023-05-09 广东电网有限责任公司揭阳供电局 Method, device and medium for calculating axial defect length of composite insulator
CN116087345B (en) * 2023-04-06 2023-06-13 广东电网有限责任公司揭阳供电局 Method, device and medium for calculating axial defect length of composite insulator
CN116466667A (en) * 2023-04-20 2023-07-21 成都工业职业技术学院 Intelligent control method, system and storage medium for part machining

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